Сверточный вывод модели и свертка вручную на входе выборки дают разные значения

Question

Я пытаюсь восстановить выход первого слоя свертки на вводе выборки двумя способами. Во-первых, непосредственно получим выходные данные первого конвективного слоя, используя модель, изученную на входе образца. Во-вторых, вручную перемещайтесь по вводным выборкам с весами и смещениями и генерируйте выходные данные.

Когда я сравниваю результаты обоих методов, значения не совсем совпадают.

Первый метод: Обучите модель и восстановите выход первого слоя Conv на образце.

Второй метод: обучите модель, используйте изученные веса и смещения каждого фильтра и получите выходные данные слоя Conv.

# Packages, Data and Model
import numpy as np
np.random.seed(37)
import pandas as pd
import keras
import tensorflow as tf
tf.set_random_seed(89)
import random as rn
rn.seed(1254)
from keras import backend as k
session_conf = tf.ConfigProto(intra_op_parallelism_threads=1, inter_op_parallelism_threads=1)
sess = tf.Session(graph = tf.get_default_graph(), config = session_conf)
k.set_session(sess)

import matplotlib.pyplot as plt
from keras.layers import *
from keras import backend as k
from keras.models import Sequential
from keras.layers import Activation
from keras.layers.core import Dense, Flatten
from keras.optimizers import Adam
from keras.metrics import categorical_crossentropy
from keras.preprocessing.image import ImageDataGenerator
from keras.layers.normalization import BatchNormalization
from keras.layers.convolutional import *
import itertools
from keras.layers.normalization import BatchNormalization
from keras.preprocessing.image import ImageDataGenerator
from keras.callbacks import ReduceLROnPlateau
from sklearn.model_selection import train_test_split
import math
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split, StratifiedKFold
from keras.models import Sequential, Model
from keras.layers import Input, Flatten, Dense, Dropout, Convolution2D, Conv2D, MaxPooling2D, Lambda, GlobalMaxPooling2D, GlobalAveragePooling2D, BatchNormalization, Activation, AveragePooling2D, Concatenate
from keras.preprocessing.image import ImageDataGenerator
from keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau
from keras.utils import np_utils
from keras.callbacks import CSVLogger
#%matplotlib inline
keras.backend.set_image_data_format('channels_last')
from keras import initializers


# DATA
# 500 training example data:: each example is 1832 dimensional long
# where dimensional element is encoded using 5 dimensional one hot encoding
# for demonstration lets's use randomly initiated integers
X = np.random.random_integers(0, 1, size=(500, 1832, 5))
y = np.random.random_integers(0, 1, size=(500, 1))

print("X.shape : ", X.shape) ## (2237, 95, 95, 1)
print("y.shape : ", y.shape) ## (2237, 1)

train_index = 450
test_index = 450

# HYPERPARAMETERS
ACTIVATION = 'relu'
KERNEL_INITIALIZER = initializers.glorot_uniform(seed=90)
BIAS_INITIALIZER='zeros'
INPUT_SHAPE=X.shape[1:]
OUTPUT_ACTIVATION = 'sigmoid'

OPTIMIZER = Adam
LEARNING_RATE = 0.0001
BETA_1 = 0.9
BETA_2 = 0.999
EPSILON = None
DECAY = 0.0
AMSGRAD = False
LOSS = 'binary_crossentropy'
METRICS = ['accuracy']

BATCH_SIZE = 64
EPOCHS = 15
SHUFFLE = True
VALIDATION_SPLIT = 0.05

FIRST_CONV_LYR = 'conv_lyr_1'
FIRST_CONV_LYR_FILTERS = 20

FIRST_DENSE_LYR = 'dense_lyr_1'
FIRST_DENSE_LYR_UNITS = 50

VALID_PADDING = 'valid'
SAME_PADDING = 'same'
USE_BIAS = True
VERBOSE = 2
DILATION_RATE=1

# MODEL
model_21 = Sequential()
model_21.add(Conv1D(filters=FIRST_CONV_LYR_FILTERS, kernel_size = (2), strides=(2), activation = ACTIVATION, 
                    padding = VALID_PADDING, input_shape =INPUT_SHAPE , kernel_initializer=KERNEL_INITIALIZER, 
                    dilation_rate=DILATION_RATE, bias_initializer=BIAS_INITIALIZER, name =FIRST_CONV_LYR ))

model_21.add(MaxPooling1D()) # input_shape=(None, 1802, 20)

model_21.add(Flatten()) #::: GMP does not need flatten()

model_21.add(Dense(1, activation = OUTPUT_ACTIVATION))

model_21.compile(optimizer=OPTIMIZER(lr = LEARNING_RATE, beta_1=BETA_1, beta_2=BETA_2, epsilon=EPSILON, decay=DECAY, 
                                     amsgrad=AMSGRAD), loss = LOSS, metrics = METRICS,) 

mdl_21 = model_21.fit(X[:train_index], y[:train_index], batch_size = BATCH_SIZE, epochs = EPOCHS, verbose = VERBOSE, 
                      shuffle=SHUFFLE, validation_split=VALIDATION_SPLIT) 

accuracy_21 = model_21.evaluate(X[test_index:], y[test_index:])

print(model_21.metrics_names[1], accuracy_21[1]*100)

print("The accuracy of Model 21 is : ", accuracy_21)

# PREDICTION
prediction_21 = model_21.predict(X[test_index:]) # predict() takes <class 'numpy.ndarray'> as input
predicted_classes_21 = model_21.predict_classes(X[test_index:])
print("The predicted classes are : ", predicted_classes_21[:, 0])
print("Actual classes are : ", y[test_index:][:, 0]) # [:, 0]



# Model Summary
model_21.summary()

Слой (тип) Выходная форма Параметр #
conv_lyr_1 (Conv1D) (нет, 916, 20) 220
max_pooling1d_1 (MaxPooling1 (Нет, 458, 20) 0
flatten_1 (Flatten) (нет, 9160) 0
density_1 (Плотный) (Нет, 1) 9161
Всего параметров: 9 381 Обучаемые параметры: 9,381 Не обучаемые параметры: 0

# Layer weights and biases
first_layer_weights = model_21.layers[0].get_weights()[0]
first_layer_biases  = model_21.layers[0].get_weights()[1]
fourth_layer_weights = model_21.layers[3].get_weights()[0]
fourth_layer_biases  = model_21.layers[3].get_weights()[1]


# Conv layer Output from Keras model using a sample test index input at index 450 
from keras.models import Model

model = model_21  # include here your original model

layer_name = 'conv_lyr_1'
intermediate_conv_layer_model = Model(inputs=model.input, outputs=model.get_layer(layer_name).output)
intermediate_conv_output = intermediate_conv_layer_model.predict(X[test_index:test_index+1])


# Manually calculating Conv layer Output using a sample test index 450 input with weights of each filter and biases of the learned model
conv_v = [[],[],[],[],[],[],[],[],[],[],[],[],[],[],[],[],[],[],[],[]]
for i in range(20):
    for j in range(0, len(X[test_index]), 2):
        conv_1 = np.sum(np.multiply(X[test_index][j:j+2], first_layer_weights[:,:,i])) + first_layer_biases[i]
        conv_1 = np.float32("{0:.8f}".format(conv_1))
        conv_v[i].append(conv_1)

# Comparing both outputs: intermediate_conv_output and conv_v[filter#]
for fil in range(20):
    print("##############################################")
    print("##############################################")
    print("Filter ", fil+1)
    for i in range(0, 916):
        #print("Index ", i)
        #print(conv_v[fil][i], intermediate_conv_output[:,i,fil][0])
        if(conv_v[fil][i] == intermediate_conv_output[:,i,fil][0]):
            print("Values equal at index :", i)
    print("\n")

Выход

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Filter  1
Values equal at index : 0
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Filter  2
Values equal at index : 2
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Filter  3
Values equal at index : 2
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Values equal at index : 37
Values equal at index : 58
Values equal at index : 71
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...... and so on excluded because of character limitation.

Почему значения для обоих выходов не равны. Еще один связанный с этим вопрос, выполняющий np.max (0, значения, превышающие размер двух последовательных конв выходов), также не дает вывод, сгенерированный слоем пула с тем же индексным номером входной выборки 450.

Заранее спасибо !!!